#
# Copyright (C) 2023, Inria
# GRAPHDECO research group, https://team.inria.fr/graphdeco
# All rights reserved.
#
# This software is free for non-commercial, research and evaluation use 
# under the terms of the LICENSE.md file.
#
# For inquiries contact  george.drettakis@inria.fr
#

import os
import torch
from random import randint
from utils.loss_utils import l1_loss, ssim
from gaussian_renderer import render, network_gui
import sys
import torch.nn.functional as F
from scene import Scene, GaussianModel
from utils.general_utils import safe_state
import uuid
from tqdm import tqdm
import torchvision
from utils.image_utils import psnr
from argparse import ArgumentParser, Namespace
from arguments import ModelParams, PipelineParams, OptimizationParams
from torchvision.utils import save_image
from render import render_set
try:
    from torch.utils.tensorboard import SummaryWriter
    TENSORBOARD_FOUND = True
except ImportError:
    TENSORBOARD_FOUND = False

def training(dataset: ModelParams, opt: OptimizationParams, pipe: PipelineParams, args: ArgumentParser):
    first_iter = 0
    tb_writer = prepare_output_and_logger(dataset)
    gaussians = GaussianModel(dataset, args)
    scene = Scene(dataset, gaussians)
    
    if args.checkpoint:
        print("Create Gaussians from checkpoint {}".format(args.checkpoint))
        scene.load(args.checkpoint)
    gaussians.training_setup(opt)

    # 设置背景颜色并放置 cuda 上
    bg_color = [1, 1, 1] if dataset.white_background else [0, 0, 0]
    background = torch.tensor(bg_color, dtype=torch.float32, device="cuda")

    iter_start = torch.cuda.Event(enable_timing = True)
    iter_end = torch.cuda.Event(enable_timing = True)

    viewpoint_stack = None
    progress_bar = tqdm(range(first_iter, opt.iterations), desc="Training progress")
    first_iter += 1
    for iteration in range(first_iter, opt.iterations + 1):        
        iter_start.record()

        # 更新 xyz 的学习率
        gaussians.update_learning_rate(iteration)
        
        # Every 1000 its we increase the levels of SH up to a maximum degree
        # 每迭代 1000 轮次将球谐的阶数 +1，直到达到最大阶数
        if iteration % 1000 == 0:
            gaussians.oneupSHdegree()

        # Pick a random Camera
        # 随机选择一个图片和对应的视角参数（内外参）
        if not viewpoint_stack:
            viewpoint_stack = scene.getTrainCameras().copy()
        viewpoint_cam = viewpoint_stack.pop(randint(0, len(viewpoint_stack)-1))

        # Render
        if (iteration - 1) == args.debug_from:
            pipe.debug = True
            
        render_pkg = render(viewpoint_cam, gaussians, pipe, background)
        
        # 返回：
        image = render_pkg["render"]
        viewspace_point_tensor = render_pkg["viewspace_points"]
        visibility_filter = render_pkg["visibility_filter"]
        radii = render_pkg["radii"]
                
        gt_image = viewpoint_cam.original_image.cuda()
        Ll1 = l1_loss(image, gt_image)
        loss = (1.0 - opt.lambda_dssim) * Ll1 + opt.lambda_dssim * (1.0 - ssim(image, gt_image))
        psnr_ = psnr(gt_image, image).mean().item()
        
        loss.backward()
        iter_end.record()
        
        if iteration % args.vis_interval == 0:
            save_dir = os.path.join(dataset.model_path, f'vis')
            os.makedirs(save_dir, exist_ok=True)
            img = torch.cat([image, gt_image], -1)
            save_image(img, os.path.join(save_dir, f"iters_{iteration:04d}.png"))

        with torch.no_grad():
            if iteration % args.save_interval == 0 or (iteration == opt.iterations): 
                scene.save(iteration)
            
            if iteration % 10 == 0:
                progress_bar.set_postfix({"PSNR": f"{psnr_:.4f}", 
                                          "PC_num": f"{gaussians.get_xyz.shape[0]}"})
                progress_bar.update(10)
            if iteration == opt.iterations:
                progress_bar.close()

            # Log and save
            if iteration % args.test_interval == 0:
                save_img = (iteration == opt.iterations)
                test_cams = scene.getTestCameras()
                train_cams = scene.getTrainCameras()
                render_set(dataset.model_path, "train", opt.iterations, train_cams, gaussians, pipe, background, save=False, psnr_only=True)
                render_set(dataset.model_path, "test", opt.iterations, test_cams, gaussians, pipe, background, save=False, psnr_only=True)
                # training_report(args, iteration, scene, render, pipe, background)
            
            # Densification
            if iteration < opt.densify_until_iter:
                # Keep track of max radii in image-space for pruning
                gaussians.max_radii2D[visibility_filter] = torch.max(gaussians.max_radii2D[visibility_filter], radii[visibility_filter])
                gaussians.add_densification_stats(viewspace_point_tensor, visibility_filter)

                if iteration > opt.densify_from_iter and iteration % opt.densification_interval == 0:
                    size_threshold = 20 if iteration > opt.opacity_reset_interval else None
                    gaussians.densify_and_prune(opt.densify_grad_threshold, 0.005, scene.cameras_extent, size_threshold)
                
                if iteration % opt.opacity_reset_interval == 0 or (dataset.white_background and iteration == opt.densify_from_iter):
                    gaussians.reset_opacity()

            # Optimizer step
            if iteration < opt.iterations:
                gaussians.optimizer.step()
                gaussians.optimizer.zero_grad(set_to_none = True)

def prepare_output_and_logger(args):    
    if not args.model_path:
        args.scene_name = args.source_path.split('/')[-1]
        args.model_path = os.path.join("./output/", args.scene_name)
        
    # Set up output folder
    print("Output folder: {}".format(args.model_path))
    os.makedirs(args.model_path, exist_ok = True)
    with open(os.path.join(args.model_path, "cfg_args"), 'w') as cfg_log_f:
        cfg_log_f.write(str(Namespace(**vars(args))))

    # Create Tensorboard writer
    tb_writer = None
    if TENSORBOARD_FOUND:
        tb_writer = SummaryWriter(args.model_path)
    else:
        print("Tensorboard not available: not logging progress")
    return tb_writer

def training_report(args, iteration, scene: Scene, renderFunc, pipe, background):
    torch.cuda.empty_cache()
    validation_configs = ({'name': 'test', 'cameras' : scene.getTestCameras()}, 
                            {'name': 'train', 'cameras' : [scene.getTrainCameras()[idx % len(scene.getTrainCameras())] for idx in range(5, 30, 5)]})

    for config in validation_configs:
        if config['cameras'] and len(config['cameras']) > 0:
            l1_test = 0.0
            psnr_test = 0.0
            for idx, viewpoint in enumerate(config['cameras']):
                image = torch.clamp(renderFunc(viewpoint, scene.gaussians, pipe, background)["render"], 0.0, 1.0)
                gt_image = torch.clamp(viewpoint.original_image.to("cuda"), 0.0, 1.0)
                assert gt_image.shape == image.shape
                l1_test += l1_loss(image, gt_image).mean().double()
                psnr_test += psnr(image, gt_image).mean().double()
            psnr_test /= len(config['cameras'])
            l1_test /= len(config['cameras'])   
            
            print(f"\n[ITER {iteration}] Evaluating {config['name']}: L1_loss={l1_test} PSNR={psnr_test}")
    torch.cuda.empty_cache()

if __name__ == "__main__":
    # Set up command line argument parser
    parser = ArgumentParser(description="Training script parameters")
    lp = ModelParams(parser)
    op = OptimizationParams(parser)
    pp = PipelineParams(parser)
    parser.add_argument('--debug_from', type=int, default=-1)
    parser.add_argument('--detect_anomaly', action='store_true', default=False)
    parser.add_argument("--test_interval", type=int, default=10000)
    parser.add_argument("--save_interval", type=int, default=10000)
    parser.add_argument("--vis_interval", type=int, default=1000)
    parser.add_argument("--quiet", action="store_true")
    parser.add_argument("--gpu", type=int, default=0)    
    parser.add_argument("--checkpoint", type=str, default="")
    args = parser.parse_args(sys.argv[1:])
    
    print("Optimizing " + args.model_path)

    # Initialize system state (RNG)
    safe_state(args.quiet)
    torch.cuda.set_device(args.gpu)

    # Start GUI server, configure and run training
    # network_gui.init(args.ip, args.port)
    torch.autograd.set_detect_anomaly(args.detect_anomaly)
    training(lp.extract(args), 
             op.extract(args), 
             pp.extract(args), 
             args)

    # All done
    print("\nTraining complete.")
